The present invention relates to a data processing system having a memory packaged therein for realizing a large-scale and fast parallel distributed processing and, more specifically, for realizing a neural network processing system.
Parallel distributed data processing using neural networks, called the "neuro-computing" (as will be shortly referred to as the "neural network processing") is noted in the field of acoustics, speech and image processing, and is described on pp. 145-168, "Parallel networks that learn to pronounce English test. Complex Systems 1 by Sejnowski, T.J., and Rosenberg, C.R. 1987, and "Neural Network Processing" published by Sangyo Tosho and edited by Hideki Asou. In neural network processing, a number of processing elements called "neurons" are connected in a network and exchange data through transfer lines called "connections" for high-grade data processing. In each neuron, the data (i.e., the outputs of the neurons) sent from another neuron are subjected to simple processes such as multiplications or summations. Since processing in the individual neurons and processing of different neurons can be carried out in parallel, the neural network processing is advantageous in principle because it offers fast data processing. Since algorithms for setting the connection weights of the neurons for a desired data processing have been proposed, data processing can be varied for the objects, as described on pp. 533-536, "Learning representations by back-propagation errors" Nature 323-9 (1986) by Rumelhart, D. E. Hinton, G.E. and Williams, R.J., and on 2nd Section of "Neural Network Processing" published by Sangyo Tosho and edited by Hideki Asou.